Alphaviruses in Panama

FOI models fitted to age-structured sero-prevalence

# ---- Datasets
dat1 <- readRDS('data/Panama2012.RDS') %>% mutate(PlaceName = 'Darien',  id = stringr::str_sub(dataset_id, 1, 3))
dat2 <- readRDS('data/Panama2017.RDS') %>% mutate(PlaceName = 'Mogue',  id = stringr::str_sub(dataset_id, 1, 3))
dat3 <- readRDS('data/Panama2012_places.RDS')%>% mutate(survey = dataset_id)


dat0   <- rbind(dat3, dat1, dat2) %>% filter(id %in% c('005'))
dat0$virus[dat0$virus=='UNA'] <- 'UNAV' 
dat0 <- dat0 %>%  mutate(counts = pos,
                         survey = paste(virus,tsur, PlaceName, id)) %>%
  arrange(desc(survey)) %>%  mutate(age_mean_f = floor((age_min + age_max)/2))
rm(dat1, dat2, dat3)

(datasets <- unique(dat0$survey))

##  [1] "VEEV 2017 Mogue 005"  "VEEV 2012 Tamar 005"  "VEEV 2012 Real 005"  
##  [4] "VEEV 2012 Pi-Pi 005"  "VEEV 2012 Merca 005"  "VEEV 2012 Darien 005"
##  [7] "VEEV 2012 Aruza 005"  "UNAV 2017 Mogue 005"  "MADV 2017 Mogue 005" 
## [10] "MADV 2012 Tamar 005"  "MADV 2012 Real 005"   "MADV 2012 Pi-Pi 005" 
## [13] "MADV 2012 Merca 005"  "MADV 2012 Darien 005" "MADV 2012 Aruza 005"

for (s in datasets)
{
  
  dat <- filter(dat0, survey == s) %>% arrange(age_mean_f) %>%
    mutate(birth_year = tsur - age_mean_f)
  
  res1  <- fFitModel(model1, dat)
  res2  <- fFitModel(model2, dat)
  
  
  plot_res1 <- fPlotModel(res1, dat, 'constant', 'uniform')
  plot_res2 <- fPlotModelDecades(res2, dat, 'decades', 'student_t')
  
  
  grid.arrange(plot_res1, plot_res2, nrow =1)

  res_survey <- list(dat  = dat,
                     mod1  = res1,
                     mod2  = res2)


  saveRDS(res_survey, paste0('res_final_10000/', s, '.RDS' ))


  rm(dat, res1, res2, plot_res1, plot_res2, res_survey)

  }